The evolution from traditional business intelligence to big data analytics has witnessed the emergence of data lakes in which data is ingested in raw form rather than into traditional data warehouses. With the increasing availability of many more pieces of information or data about each entity of interest, e.g., a customer, often from diverse sources (social media, mobility, internet-of-things), fusing, visualizing and deriving insights from such data may pose a number of challenges. The challenges are due to disparate datasets which often lack a natural join key. Also, the datasets may describe measures at different levels of granularity, e.g., individual versus aggregate data, different datasets may be derived from physically distinct populations. Moreover, once data has been fused, queries are often an inefficient and inaccurate mechanism to derive insight from high-dimensional data.